The present invention generally relates to wireless communication devices, and particularly relates to suppressing interference from a received signal using combining weights derived from impairment covariance estimates.
Direct Sequence Code Division Multiple Access (DS-CDMA) systems use two different codes for separating devices and/or channels when transmitting data. Scrambling codes are pseudo-noise sequences used to separate base stations, cells or sectors from each other in the downlink. In the uplink, scrambling codes separate users. Channelization codes are orthogonal sequences used to separate different channels in a particular cell or sector in the downlink or to separate different parallel streams of data intended for one user in either the uplink or downlink. As such, channelization codes channelize user-specific data onto respective channels and scrambling codes scramble the channelized data.
Multipath fading causes loss of orthogonality between different channelization codes, thus causing signal interference. This interference can be self-interference (e.g., between codes assigned to the same user) as well as inter-user-interference. Signal interference limits performance in DS-CDMA systems such as Wideband-CDMA (W-CDMA), High Speed Downlink Packet Access (HSDPA), CDMA2000 and 1XEV-DO systems. Particularly, capacity and high data-rate coverage are adversely affected. Signal interference may be self-induced where other desired symbols sent in parallel (multicode) or in series (previous, next symbols) interfere with a symbol of interest. Other-user symbols also cause interference and may be sent from the same base station (own-cell interference) or from a different base station (other-cell interference).
Conventional wireless receivers account for impairment (interference and noise) correlations between interfering signals and a signal of interest when processing a received signal. Accounting for impairment correlations enables a receiver to better suppress interference from a received signal, thus improving performance. For example, in a Generalized RAKE (G-RAKE) receiver, some signal processing “fingers” are placed on signal path delays of a multipath fading channel for optimizing signal energy collection while other fingers are placed off signal path delays to suppress interference. Finger outputs are combined to form symbol estimates by weighting each component based on impairment correlations.
That is, G-RAKE receivers suppress interference by optimally combining components of a received signal based on impairment correlations. The combining weights may also be used to estimate received signal quality, e.g., by calculating a Signal-to-Interference Ratio (SIR). (The same or similar signal quality measures are sometimes referred to as Signal-to-Interference-plus-Noise Ratios, or SINRs) SIR information is provided to the corresponding base station for use in optimizing radio resources, e.g., by adjusting the power allocated to different channelization codes.
G-RAKE combining weights and SIR estimates are derived from channel response and impairment correlation estimates. Channel response and impairment correlation estimates are ascertained at least in part from demodulated and de-spread pilot symbols transmitted over a common channel (e.g., the Common Pilot Channel, or CPICH, in Wideband-CDMA systems). In a parametric estimation approach to the impairment correlation estimation process, interference and noise powers are estimated by fitting interference and noise correlation terms of a parametric model of the impairment to a pilot symbol-based measured impairment. In non-parametric approaches, estimates of the impairment are calculated directly from pilot-based measurements.
The result of either impairment estimation process is a covariance matrix, conventionally denoted R, the elements of which represent the covariance between any pair of fingers in the G-RAKE receiver (or, equivalently, between any pair of taps in a chip-equalizer receiver). Accordingly, in a typical W-CDMA time-domain equalizer, the combining weights used to form the de-spread symbols from the RAKE fingers can be written as:w=R−1h,  (1)where h are the net channel estimates (propagation channel convolved with the responses of the receiver and transmit filters). Here h is a column vector, and each entry represents a path delay or finger used in the demodulation.
Each element in, corresponding to fingers f1 and f1 in the covariance matrix, can be computed by correlating samples of the type (hf1,s−hf1)(hf2,s−hf2), and averaging over a sufficient amount of time. Here, hf1,s is the sample observed directly from the channel, that is, before filtering or “de-noising,” while hf1 is a corresponding de-noised channel estimate. If the physical channel used to compute hf1 is of zero power, then the above expression reduces to hf1,s(hf2,s)*. More details of this process are given in G. Bottomley, et al., “A generalized RAKE receiver for interference suppression,” IEEE Journal on Selected Areas of Communication, Vol. 18, no. 8, August 2000, and in U.S. Pat. No. 6,714,585, issued Mar. 30, 2004 and titled “RAKE Combining Methods and Apparatus using Weighting Factors Derived from Knowledge of Spreading Spectrum Signal Characteristics,” the entire contents of which are incorporated by reference herein.
Typically, the samples hf,s are estimated based only on the Common Pilot Channel (CPICH). A general problem with this type of equalizer is that the amount of noise in the matrix elements of R may be quite high. In particular, a drawback with the standard method of estimating the samples. hf1,s. based on the CPICH is that the once the number of fingers used in the demodulation becomes large (e.g., 20 or more) the noise in R becomes so large that the use of additional fingers does not improve receiver performance.